Table of Contents

  • 1  Interconnect Churn
    • 1.1  Purpose
    • 1.2  Feature Engineering
    • 1.3  Clustering
    • 1.4  Modeling
    • 1.5  Final Work Plan
    • 1.6  Feature Engineering
    • 1.7  EDA
    • 1.8  Preprocessing
      • 1.8.1  K Means Clustering
    • 1.9  Modeling
      • 1.9.1  Classifier Pipeline
        • 1.9.1.1  ADA Pipeline Feature Importance
        • 1.9.1.2  XGBoost Pipeline Feature Importance
        • 1.9.1.3  LGBM Pipeline Feature Importance
        • 1.9.1.4  Gradient Boost Pipeline Feature Importance
        • 1.9.1.5  CatBoost Pipeline Feature Importance
      • 1.9.2  Comparing Models
    • 1.10  Final Model
      • 1.10.1  Final Model Evaluation Metric
      • 1.10.2  Final Model Feature Importances
    • 1.11  Overall Conclusions
    • 1.12  Solution Report

Interconnect Churn¶

Purpose¶

The telecom operator Interconnect would like to forecast churn of their clients. To ensure loyalty, those who are predicted to leave will be offered promotional codes and special plans.

In [ ]:
# import libraries
import pandas as pd  
import numpy as np  
import plotly_express as px  
import seaborn as sns
from ydata_profiling.profile_report import ProfileReport
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import StandardScaler 
from sklearn.model_selection import cross_val_score, GridSearchCV, KFold, train_test_split
from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, VotingClassifier
from catboost import CatBoostClassifier, Pool, cv 
from xgboost import XGBClassifier
import lightgbm as lgb
from imblearn.over_sampling import SMOTE
from imblearn.pipeline import Pipeline
from sklearn.utils import shuffle
from sklearn.metrics import roc_auc_score, roc_curve, auc
In [ ]:
# load data
contract = pd.read_csv('datasets/contract.csv', parse_dates=[1, 2])
internet = pd.read_csv('datasets/internet.csv')
personal = pd.read_csv('datasets/personal.csv')
phone = pd.read_csv('datasets/phone.csv')
In [ ]:
# EDA profile reports
con_prof = ProfileReport(contract, title= 'Contract Profile')
int_prof = ProfileReport(internet, title= 'Internet Profile')
per_prof = ProfileReport(personal, title= 'Personal Profile')
pho_prof = ProfileReport(phone, title= 'Contract Profile')
In [ ]:
# contract profile report
con_prof

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The contract dataset contains a few features, and the target variable in the form of end date. If the customer has churned, there will be an end date, otherwise the customer is still with the company. Therefore, we can see class imbalance, which will need to be rectified to improve the score of the model. The column names will need to be changed to lowercase, and underscores will be added to separate words. Customer Id is a common column among all the datasets, therefore we will use that to combine them. Customer id contains 7043 values, and all are unique. Of note, begin date has moderate correlation with type. This dataset does not contain missing values, ut has categorical columns that may need encoding.



















In [ ]:



     


# internet profile report
int_prof



     






















    
    








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The internet dataset contains a customer id column with a few other categorical features. The dataset contains 5517 observations with no missing values. This profile report also shows us the distributions of the different features. Most of the features have two distinct values, the absence or presence of a service.



















In [ ]:



     


# personal profile report
per_prof



     






















    
    








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EDA shows us that gender among the personal dataset is even. A vast majority of customers are not senior citizens, and the distribution of having a partner is fairly even. A majority of customers have dependents. The dataset is free of missing values.



















In [ ]:



     


# phone profile report
pho_prof



     






















    
    








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This dataset has one column that has a yes or no value for multiple lines, paired with customer id. The multiple lines feature is fairly balanced with no values missing.























Feature Engineering¶






















The different datasets will be combined into one full dataset by joining on the column id. The dates column will be parsed to create new features. Furthermore, the end date column will be label encoded.























Clustering¶






















Analysis will be done to se if the the full dataset contains clusters of customers.























Modeling¶






















Some boosting models work with categorical features natively, but we may have to label encode those features. We will also need to scale the data, and balance the classes for churn by upsampling. We will then test across a couple of models, and optimize the AUCROC score. Finally, we will see if a voting classifier improves the AUCROC further.























Final Work Plan¶






















    

  1. Read data
    2. 

  2. Combine data
    3. 

  3. Feature engineering/ change column names and types
    4. 

  4. EDA
    5. 

  5. KNN clustering
    6. 

  6. Preprocessing, upscaling, standard scaling
    7. 

  7. Modeling with boost methods and cross validation
    8. 

  8. Hyperparameter tuning
    9. 

  9. Feature importance
    10. 

  10. Visualizing modeling results 
    11. 

  11. Voting Classifier 
    12. 

  12. Visualize final model, feature importance
    13. 

  13. Conclude
    14. 
























Feature Engineering¶


















In [ ]:



     


# contract columns
contract.columns



     






















    
    
Out[ ]:








Index(['customerID', 'BeginDate', 'EndDate', 'Type', 'PaperlessBilling',
       'PaymentMethod', 'MonthlyCharges', 'TotalCharges'],
      dtype='object')
























In [ ]:



     


# change column names
contract.columns = ['customer_id', 'begin_date', 'end_date', 'type', 'paperless_billing', 'payment_method', 'monthly_charges', 'total_charges']
internet.columns = [ 'customer_id', 'internet_service', 'online_security', 'online_backup', 'device_protection', 'tech_support', 'streaming_tv', 'streaming_movies']
personal.columns = ['customer_id', 'gender', 'senior_citizen', 'partner', 'dependents']
phone.columns = ['customer_id', 'multiple_lines']



     



















In [ ]:



     


# new contract columns
contract.columns



     






















    
    
Out[ ]:








Index(['customer_id', 'begin_date', 'end_date', 'type', 'paperless_billing',
       'payment_method', 'monthly_charges', 'total_charges'],
      dtype='object')
























In [ ]:



     


# new internet columns
internet.columns



     






















    
    
Out[ ]:








Index(['customer_id', 'internet_service', 'online_security', 'online_backup',
       'device_protection', 'tech_support', 'streaming_tv',
       'streaming_movies'],
      dtype='object')
























In [ ]:



     


# new personal columns
personal.columns



     






















    
    
Out[ ]:








Index(['customer_id', 'gender', 'senior_citizen', 'partner', 'dependents'], dtype='object')
























In [ ]:



     


# new phone columns
phone.columns



     






















    
    
Out[ ]:








Index(['customer_id', 'multiple_lines'], dtype='object')
























In [ ]:



     


# first merge
df2 = contract.merge(internet, how='left')



     



















In [ ]:



     


# results of first merge 
df2.columns



     






















    
    
Out[ ]:








Index(['customer_id', 'begin_date', 'end_date', 'type', 'paperless_billing',
       'payment_method', 'monthly_charges', 'total_charges',
       'internet_service', 'online_security', 'online_backup',
       'device_protection', 'tech_support', 'streaming_tv',
       'streaming_movies'],
      dtype='object')
























In [ ]:



     


# second merge
df3 = df2.merge(personal, how='left')



     



















In [ ]:



     


# second merge results
df3.columns



     






















    
    
Out[ ]:








Index(['customer_id', 'begin_date', 'end_date', 'type', 'paperless_billing',
       'payment_method', 'monthly_charges', 'total_charges',
       'internet_service', 'online_security', 'online_backup',
       'device_protection', 'tech_support', 'streaming_tv', 'streaming_movies',
       'gender', 'senior_citizen', 'partner', 'dependents'],
      dtype='object')
























In [ ]:



     


# final merge
df = df3.merge(phone, how='left')



     



















In [ ]:



     


# final dataset columns
df.columns



     






















    
    
Out[ ]:








Index(['customer_id', 'begin_date', 'end_date', 'type', 'paperless_billing',
       'payment_method', 'monthly_charges', 'total_charges',
       'internet_service', 'online_security', 'online_backup',
       'device_protection', 'tech_support', 'streaming_tv', 'streaming_movies',
       'gender', 'senior_citizen', 'partner', 'dependents', 'multiple_lines'],
      dtype='object')
























In [ ]:



     


# info on the columns
df.info()



     






















    
    






<class 'pandas.core.frame.DataFrame'>
Int64Index: 7043 entries, 0 to 7042
Data columns (total 20 columns):
 #   Column             Non-Null Count  Dtype         
---  ------             --------------  -----         
 0   customer_id        7043 non-null   object        
 1   begin_date         7043 non-null   datetime64[ns]
 2   end_date           7043 non-null   object        
 3   type               7043 non-null   object        
 4   paperless_billing  7043 non-null   object        
 5   payment_method     7043 non-null   object        
 6   monthly_charges    7043 non-null   float64       
 7   total_charges      7043 non-null   object        
 8   internet_service   5517 non-null   object        
 9   online_security    5517 non-null   object        
 10  online_backup      5517 non-null   object        
 11  device_protection  5517 non-null   object        
 12  tech_support       5517 non-null   object        
 13  streaming_tv       5517 non-null   object        
 14  streaming_movies   5517 non-null   object        
 15  gender             7043 non-null   object        
 16  senior_citizen     7043 non-null   int64         
 17  partner            7043 non-null   object        
 18  dependents         7043 non-null   object        
 19  multiple_lines     6361 non-null   object        
dtypes: datetime64[ns](1), float64(1), int64(1), object(17)
memory usage: 1.1+ MB
























In [ ]:



     


# look at df
df.head()



     






















    
    
Out[ ]:











  
    

      
      customer_id
      begin_date
      end_date
      type
      paperless_billing
      payment_method
      monthly_charges
      total_charges
      internet_service
      online_security
      online_backup
      device_protection
      tech_support
      streaming_tv
      streaming_movies
      gender
      senior_citizen
      partner
      dependents
      multiple_lines
    

  
  
    

      0
      7590-VHVEG
      2020-01-01
      No
      Month-to-month
      Yes
      Electronic check
      29.85
      29.85
      DSL
      No
      Yes
      No
      No
      No
      No
      Female
      0
      Yes
      No
      NaN
    

    

      1
      5575-GNVDE
      2017-04-01
      No
      One year
      No
      Mailed check
      56.95
      1889.5
      DSL
      Yes
      No
      Yes
      No
      No
      No
      Male
      0
      No
      No
      No
    

    

      2
      3668-QPYBK
      2019-10-01
      2019-12-01 00:00:00
      Month-to-month
      Yes
      Mailed check
      53.85
      108.15
      DSL
      Yes
      Yes
      No
      No
      No
      No
      Male
      0
      No
      No
      No
    

    

      3
      7795-CFOCW
      2016-05-01
      No
      One year
      No
      Bank transfer (automatic)
      42.30
      1840.75
      DSL
      Yes
      No
      Yes
      Yes
      No
      No
      Male
      0
      No
      No
      NaN
    

    

      4
      9237-HQITU
      2019-09-01
      2019-11-01 00:00:00
      Month-to-month
      Yes
      Electronic check
      70.70
      151.65
      Fiber optic
      No
      No
      No
      No
      No
      No
      Female
      0
      No
      No
      No
    

  



























In [ ]:



     


# extracting new features from begin date
df['year'] = df.begin_date.dt.year
df['month'] = df.begin_date.dt.month
df['day'] = df.begin_date.dt.day
df['quarter'] = df.begin_date.dt.quarter



     



















In [ ]:



     


# create churn column
df['churn'] = np.where(df['end_date'] == 'No', 0, 1)



     



















In [ ]:



     


# drop ed_date for churn 
df.drop(columns=['end_date', 'customer_id', 'begin_date'], inplace=True)



     



















In [ ]:



     


# look at the data
df.head()



     






















    
    
Out[ ]:











  
    

      
      type
      paperless_billing
      payment_method
      monthly_charges
      total_charges
      internet_service
      online_security
      online_backup
      device_protection
      tech_support
      ...
      gender
      senior_citizen
      partner
      dependents
      multiple_lines
      year
      month
      day
      quarter
      churn
    

  
  
    

      0
      Month-to-month
      Yes
      Electronic check
      29.85
      29.85
      DSL
      No
      Yes
      No
      No
      ...
      Female
      0
      Yes
      No
      NaN
      2020
      1
      1
      1
      0
    

    

      1
      One year
      No
      Mailed check
      56.95
      1889.5
      DSL
      Yes
      No
      Yes
      No
      ...
      Male
      0
      No
      No
      No
      2017
      4
      1
      2
      0
    

    

      2
      Month-to-month
      Yes
      Mailed check
      53.85
      108.15
      DSL
      Yes
      Yes
      No
      No
      ...
      Male
      0
      No
      No
      No
      2019
      10
      1
      4
      1
    

    

      3
      One year
      No
      Bank transfer (automatic)
      42.30
      1840.75
      DSL
      Yes
      No
      Yes
      Yes
      ...
      Male
      0
      No
      No
      NaN
      2016
      5
      1
      2
      0
    

    

      4
      Month-to-month
      Yes
      Electronic check
      70.70
      151.65
      Fiber optic
      No
      No
      No
      No
      ...
      Female
      0
      No
      No
      No
      2019
      9
      1
      3
      1
    

  



5 rows × 22 columns


























In [ ]:



     


# check for missing values
df.isna().sum()



     






















    
    
Out[ ]:








type                    0
paperless_billing       0
payment_method          0
monthly_charges         0
total_charges           0
internet_service     1526
online_security      1526
online_backup        1526
device_protection    1526
tech_support         1526
streaming_tv         1526
streaming_movies     1526
gender                  0
senior_citizen          0
partner                 0
dependents              0
multiple_lines        682
year                    0
month                   0
day                     0
quarter                 0
churn                   0
dtype: int64
























In [ ]:



     


# fill missing values with No, representing no service
df.fillna('No', inplace=True)



     



















In [ ]:



     


# recheck for missing values
df.isna().sum().sum()



     






















    
    
Out[ ]:








0
























In [ ]:



     


# we found empty strings
df.query("total_charges==' '")



     






















    
    
Out[ ]:











  
    

      
      type
      paperless_billing
      payment_method
      monthly_charges
      total_charges
      internet_service
      online_security
      online_backup
      device_protection
      tech_support
      ...
      gender
      senior_citizen
      partner
      dependents
      multiple_lines
      year
      month
      day
      quarter
      churn
    

  
  
    

      488
      Two year
      Yes
      Bank transfer (automatic)
      52.55
      
      DSL
      Yes
      No
      Yes
      Yes
      ...
      Female
      0
      Yes
      Yes
      No
      2020
      2
      1
      1
      0
    

    

      753
      Two year
      No
      Mailed check
      20.25
      
      No
      No
      No
      No
      No
      ...
      Male
      0
      No
      Yes
      No
      2020
      2
      1
      1
      0
    

    

      936
      Two year
      No
      Mailed check
      80.85
      
      DSL
      Yes
      Yes
      Yes
      No
      ...
      Female
      0
      Yes
      Yes
      No
      2020
      2
      1
      1
      0
    

    

      1082
      Two year
      No
      Mailed check
      25.75
      
      No
      No
      No
      No
      No
      ...
      Male
      0
      Yes
      Yes
      Yes
      2020
      2
      1
      1
      0
    

    

      1340
      Two year
      No
      Credit card (automatic)
      56.05
      
      DSL
      Yes
      Yes
      Yes
      Yes
      ...
      Female
      0
      Yes
      Yes
      No
      2020
      2
      1
      1
      0
    

    

      3331
      Two year
      No
      Mailed check
      19.85
      
      No
      No
      No
      No
      No
      ...
      Male
      0
      Yes
      Yes
      No
      2020
      2
      1
      1
      0
    

    

      3826
      Two year
      No
      Mailed check
      25.35
      
      No
      No
      No
      No
      No
      ...
      Male
      0
      Yes
      Yes
      Yes
      2020
      2
      1
      1
      0
    

    

      4380
      Two year
      No
      Mailed check
      20.00
      
      No
      No
      No
      No
      No
      ...
      Female
      0
      Yes
      Yes
      No
      2020
      2
      1
      1
      0
    

    

      5218
      One year
      Yes
      Mailed check
      19.70
      
      No
      No
      No
      No
      No
      ...
      Male
      0
      Yes
      Yes
      No
      2020
      2
      1
      1
      0
    

    

      6670
      Two year
      No
      Mailed check
      73.35
      
      DSL
      No
      Yes
      Yes
      Yes
      ...
      Female
      0
      Yes
      Yes
      Yes
      2020
      2
      1
      1
      0
    

    

      6754
      Two year
      Yes
      Bank transfer (automatic)
      61.90
      
      DSL
      Yes
      Yes
      No
      Yes
      ...
      Male
      0
      No
      Yes
      Yes
      2020
      2
      1
      1
      0
    

  



11 rows × 22 columns


























In [ ]:



     


# percentage of missing values
df.query("total_charges==' '")['total_charges'].count()/len(df) * 100



     






















    
    
Out[ ]:








0.1561834445548772




























Less than 0.2 % of the data has missing total charges.



















In [ ]:



     


# drop those rows where total charges is an empty string
df = df.drop(df[df['total_charges'] == ' '].index)



     























Initially, we thought to fill in these missing total charges with zero, but that would mean we made an inference that these customers did not use any services during their contract. We could have tried imputing the values, but since it represents only 0.2% of the data, we will drop it.



















In [ ]:



     


# checking proper implementation
df.query("total_charges==' '")



     






















    
    
Out[ ]:











  
    

      
      type
      paperless_billing
      payment_method
      monthly_charges
      total_charges
      internet_service
      online_security
      online_backup
      device_protection
      tech_support
      ...
      gender
      senior_citizen
      partner
      dependents
      multiple_lines
      year
      month
      day
      quarter
      churn
    

  
  
  



0 rows × 22 columns


























In [ ]:



     


# convert total charges to float
df.total_charges = pd.to_numeric(df.total_charges)



     



















In [ ]:



     


# looking at dtypes of columns
df.info()



     






















    
    






<class 'pandas.core.frame.DataFrame'>
Int64Index: 7032 entries, 0 to 7042
Data columns (total 22 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   type               7032 non-null   object 
 1   paperless_billing  7032 non-null   object 
 2   payment_method     7032 non-null   object 
 3   monthly_charges    7032 non-null   float64
 4   total_charges      7032 non-null   float64
 5   internet_service   7032 non-null   object 
 6   online_security    7032 non-null   object 
 7   online_backup      7032 non-null   object 
 8   device_protection  7032 non-null   object 
 9   tech_support       7032 non-null   object 
 10  streaming_tv       7032 non-null   object 
 11  streaming_movies   7032 non-null   object 
 12  gender             7032 non-null   object 
 13  senior_citizen     7032 non-null   int64  
 14  partner            7032 non-null   object 
 15  dependents         7032 non-null   object 
 16  multiple_lines     7032 non-null   object 
 17  year               7032 non-null   int64  
 18  month              7032 non-null   int64  
 19  day                7032 non-null   int64  
 20  quarter            7032 non-null   int64  
 21  churn              7032 non-null   int32  
dtypes: float64(2), int32(1), int64(5), object(14)
memory usage: 1.2+ MB




























We added a few features to the dataset by parsing the dates, and separating them into their individual components. We then converted the end date column into a binary column named churn. After combining the datasets, we introduced missing values. We dropped those missing values so we could convert total charges into a float.























EDA¶


















In [ ]:



     


# summart stats 
df.describe()



     






















    
    
Out[ ]:











  
    

      
      monthly_charges
      total_charges
      senior_citizen
      year
      month
      day
      quarter
      churn
    

  
  
    

      count
      7032.000000
      7032.000000
      7032.000000
      7032.00000
      7032.000000
      7032.0
      7032.000000
      7032.000000
    

    

      mean
      64.798208
      2283.300441
      0.162400
      2016.87372
      6.428185
      1.0
      2.471985
      0.265785
    

    

      std
      30.085974
      2266.771362
      0.368844
      1.96824
      3.576972
      0.0
      1.157273
      0.441782
    

    

      min
      18.250000
      18.800000
      0.000000
      2013.00000
      1.000000
      1.0
      1.000000
      0.000000
    

    

      25%
      35.587500
      401.450000
      0.000000
      2015.00000
      3.000000
      1.0
      1.000000
      0.000000
    

    

      50%
      70.350000
      1397.475000
      0.000000
      2017.00000
      6.000000
      1.0
      2.000000
      0.000000
    

    

      75%
      89.862500
      3794.737500
      0.000000
      2019.00000
      10.000000
      1.0
      4.000000
      1.000000
    

    

      max
      118.750000
      8684.800000
      1.000000
      2020.00000
      12.000000
      1.0
      4.000000
      1.000000
    

  



























In [ ]:



     


# correlations
df.corr()



     






















    
    
Out[ ]:











  
    

      
      monthly_charges
      total_charges
      senior_citizen
      year
      month
      day
      quarter
      churn
    

  
  
    

      monthly_charges
      1.000000
      0.651065
      0.219874
      -0.263086
      -0.019613
      NaN
      -0.029945
      0.192858
    

    

      total_charges
      0.651065
      1.000000
      0.102411
      -0.819304
      -0.196624
      NaN
      -0.209060
      -0.199484
    

    

      senior_citizen
      0.219874
      0.102411
      1.000000
      -0.027386
      0.028569
      NaN
      0.020656
      0.150541
    

    

      year
      -0.263086
      -0.819304
      -0.027386
      1.000000
      0.084872
      NaN
      0.103909
      0.293770
    

    

      month
      -0.019613
      -0.196624
      0.028569
      0.084872
      1.000000
      NaN
      0.974156
      0.180522
    

    

      day
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
    

    

      quarter
      -0.029945
      -0.209060
      0.020656
      0.103909
      0.974156
      NaN
      1.000000
      0.173829
    

    

      churn
      0.192858
      -0.199484
      0.150541
      0.293770
      0.180522
      NaN
      0.173829
      1.000000
    

  



























In [ ]:



     


# target values
df.churn.value_counts()



     






















    
    
Out[ ]:








0    5163
1    1869
Name: churn, dtype: int64




























We see summary statistics on the full dataset. Looking at correlations, we see the begin date year has the strongest correlation with churn amongst the other features, yet is is a weak corelation. The monthly charges have a moderate negative correlation with the year.























Preprocessing¶


















In [ ]:



     


# reset index 
df = df.reset_index(drop=True)



     



















In [ ]:



     


# encoding categorical columns
columns = ['type', 'paperless_billing',
       'payment_method',
       'internet_service', 'online_security', 'online_backup',
       'device_protection', 'tech_support', 'streaming_tv', 'streaming_movies',
       'gender', 'senior_citizen', 'partner', 'dependents', 'multiple_lines']
encoder = OrdinalEncoder()
df_ordinal = pd.DataFrame(encoder.fit_transform(df[columns]), columns=df[columns].columns)



     



















In [ ]:



     


# ordinal dataset
df_ordinal.head()



     






















    
    
Out[ ]:











  
    

      
      type
      paperless_billing
      payment_method
      internet_service
      online_security
      online_backup
      device_protection
      tech_support
      streaming_tv
      streaming_movies
      gender
      senior_citizen
      partner
      dependents
      multiple_lines
    

  
  
    

      0
      0.0
      1.0
      2.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
    

    

      1
      1.0
      0.0
      3.0
      0.0
      1.0
      0.0
      1.0
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
    

    

      2
      0.0
      1.0
      3.0
      0.0
      1.0
      1.0
      0.0
      0.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
    

    

      3
      1.0
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      1.0
      0.0
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
    

    

      4
      0.0
      1.0
      2.0
      1.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
    

  



























In [ ]:



     


# replace categorical columns with ordinal columns
df[columns] = df_ordinal



     



















In [ ]:



     


# look at resulting dataframe
df.head()



     






















    
    
Out[ ]:











  
    

      
      type
      paperless_billing
      payment_method
      monthly_charges
      total_charges
      internet_service
      online_security
      online_backup
      device_protection
      tech_support
      ...
      gender
      senior_citizen
      partner
      dependents
      multiple_lines
      year
      month
      day
      quarter
      churn
    

  
  
    

      0
      0.0
      1.0
      2.0
      29.85
      29.85
      0.0
      0.0
      1.0
      0.0
      0.0
      ...
      0.0
      0.0
      1.0
      0.0
      0.0
      2020
      1
      1
      1
      0
    

    

      1
      1.0
      0.0
      3.0
      56.95
      1889.50
      0.0
      1.0
      0.0
      1.0
      0.0
      ...
      1.0
      0.0
      0.0
      0.0
      0.0
      2017
      4
      1
      2
      0
    

    

      2
      0.0
      1.0
      3.0
      53.85
      108.15
      0.0
      1.0
      1.0
      0.0
      0.0
      ...
      1.0
      0.0
      0.0
      0.0
      0.0
      2019
      10
      1
      4
      1
    

    

      3
      1.0
      0.0
      0.0
      42.30
      1840.75
      0.0
      1.0
      0.0
      1.0
      1.0
      ...
      1.0
      0.0
      0.0
      0.0
      0.0
      2016
      5
      1
      2
      0
    

    

      4
      0.0
      1.0
      2.0
      70.70
      151.65
      1.0
      0.0
      0.0
      0.0
      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      0.0
      2019
      9
      1
      3
      1
    

  



5 rows × 22 columns


























In [ ]:



     


# features and target
X = df.drop(columns='churn')
y = df.churn



     



















In [ ]:



     


# train test split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, random_state=19)



     



















In [ ]:



     


# target value counts
y.value_counts()



     






















    
    
Out[ ]:








0    5163
1    1869
Name: churn, dtype: int64
























In [ ]:



     


# look at training features
X_train.head()



     






















    
    
Out[ ]:











  
    

      
      type
      paperless_billing
      payment_method
      monthly_charges
      total_charges
      internet_service
      online_security
      online_backup
      device_protection
      tech_support
      ...
      streaming_movies
      gender
      senior_citizen
      partner
      dependents
      multiple_lines
      year
      month
      day
      quarter
    

  
  
    

      2272
      0.0
      1.0
      2.0
      109.15
      6941.20
      1.0
      0.0
      1.0
      1.0
      1.0
      ...
      1.0
      1.0
      0.0
      0.0
      0.0
      1.0
      2014
      8
      1
      3
    

    

      4321
      0.0
      1.0
      2.0
      51.10
      711.15
      0.0
      0.0
      0.0
      0.0
      0.0
      ...
      0.0
      0.0
      0.0
      1.0
      0.0
      1.0
      2018
      11
      1
      4
    

    

      3545
      0.0
      1.0
      2.0
      79.60
      79.60
      1.0
      0.0
      0.0
      0.0
      0.0
      ...
      1.0
      0.0
      0.0
      0.0
      1.0
      0.0
      2019
      9
      1
      3
    

    

      3940
      0.0
      0.0
      1.0
      55.80
      300.40
      0.0
      1.0
      0.0
      1.0
      0.0
      ...
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      2019
      9
      1
      3
    

    

      125
      2.0
      1.0
      2.0
      88.95
      3027.65
      1.0
      1.0
      0.0
      0.0
      1.0
      ...
      0.0
      1.0
      0.0
      0.0
      0.0
      0.0
      2017
      5
      1
      2
    

  



5 rows × 21 columns






























First, we encoding all the categorical columns with label encoding. we followed up by separating the data into features and targets. We then noticed the class imbalance in churn, sow e used upsampling to rectify this. Then, we concluded preprocessing with the standard scaling on the numerical columns. Looking at our final training dataset, we are prepared for modeling.























K Means Clustering¶


















In [ ]:



     


# make copy of df
df_copy = df.copy()



     



















In [ ]:



     


# K means
from sklearn.cluster import KMeans
km = KMeans(n_clusters=2, random_state=19)
km.fit(df_copy)

print('Cluster centroids:')
print(km.cluster_centers_)
print()
print(km.inertia_)



     






















    
    






Cluster centroids:
[[1.17328865e+00 7.01292484e-01 1.12733365e+00 9.12255625e+01
  5.36420701e+03 6.68262326e-01 5.42364768e-01 6.74964098e-01
  6.84059359e-01 5.57204404e-01 7.25227382e-01 7.28099569e-01
  4.98803255e-01 2.12063188e-01 6.83580661e-01 3.35088559e-01
  7.24269986e-01 2.01474964e+03 5.65581618e+00 1.00000000e+00
  2.20631881e+00 1.53662039e-01]
 [4.83714344e-01 5.46833907e-01 1.76168319e+00 5.36295367e+01
  9.81254350e+02 9.58931823e-01 1.78434149e-01 2.05340886e-01
  2.00080923e-01 1.77220312e-01 2.40339875e-01 2.44790613e-01
  5.07181873e-01 1.41412098e-01 3.97531863e-01 2.83026502e-01
  2.94153348e-01 2.01777139e+03 6.75460247e+00 1.00000000e+00
  2.58426057e+00 3.13170140e-01]]

7922736403.502683
























In [ ]:



     


# pairplot 

centroids = pd.DataFrame(km.cluster_centers_, columns=df_copy.columns)
df_copy['label'] = km.labels_.astype(str)
centroids['label'] = ['0 centroid', '1 centroid']
data_all = pd.concat([df_copy, centroids], ignore_index=True)

# Plot the graph
sns.pairplot(data_all, hue='label', diag_kind='hist')



     






















    
    
Out[ ]:








<seaborn.axisgrid.PairGrid at 0x19a290cddc0>










    
    



































Looking at the pairplot, we can clearly see the two clusters in various category pairs.























Modeling¶






















Classifier Pipeline¶


















In [ ]:



     


# K fold cross val
kf = KFold(n_splits=5, shuffle=True, random_state=19)



     























We initially used K-fold cross validation for hyperparameter tuning using gridsearch cv.



















In [ ]:



     


# Classifier pipeline
pipe_ada = Pipeline([('scalar1', StandardScaler()), ('oversampler1', SMOTE(random_state=19)), ('ada_classifier', AdaBoostClassifier(n_estimators=500,  random_state=19, learning_rate=0.1))])

pipe_xg = Pipeline([('scalar1', StandardScaler()), ('oversampler1', SMOTE(random_state=19)), ('xg_classifier', XGBClassifier(learning_rate=0.5, max_depth=5, n_estimators=1500))])

pipe_lgbm = Pipeline([('scalar1', StandardScaler()), ('oversampler1', SMOTE(random_state=19)), ('lgbm_classifier', lgb.LGBMClassifier(num_boost_round=500, objective='binary', metric='auc', random_state=19, learning_rate=0.1, n_estimators=350, num_leaves=30, max_depth=20, boosting_type='gbdt'))])

pipe_gb = Pipeline([('scalar1', StandardScaler()), ('oversampler1', SMOTE(random_state=19)), ('gb_classifier', GradientBoostingClassifier(n_estimators=1500, learning_rate=0.1, random_state=19, max_depth=5))])

pipe_cat = Pipeline([('scalar1', StandardScaler()), ('oversampler1', SMOTE(random_state=19)), ('cat_classifier', CatBoostClassifier(task_type='GPU', loss_function='Logloss', eval_metric='AUC', iterations=1500, random_seed=19))])

pipe_vote = Pipeline([('scalar1', StandardScaler()), ('oversampler1', SMOTE(random_state=19)), ('vote_classifier', VotingClassifier(estimators=[('ada', pipe_ada), ('xgb', pipe_xg), ('lgb', pipe_lgbm), ('cat', pipe_cat)], verbose=1, voting='soft'))])

pipelines = [pipe_ada, pipe_xg, pipe_lgbm, pipe_gb, pipe_cat, pipe_vote]

best_auc = 0
best_classifier = 0
best_pipeline = ""

pipe_dict = {0: 'ADA Boost', 1: 'XG Boost', 2: 'Light GBM', 3: 'Gradient Boost', 4: 'Cat Boost', 5: 'Voting Classifier'}

# Use cross-validation to evaluate the models
for i, model in enumerate(pipelines):
    scores = cross_val_score(model, X_train, y_train, cv=5, scoring='roc_auc')
    print('{} Cross-Validation AUC-ROC: {:.2f}'.format(pipe_dict[i], scores.mean()))
    if scores.mean() > best_auc:
        best_auc = scores.mean()
        best_pipeline = model
        best_classifier = i

# Print the best classifier
print('\nClassifier with the best AUC-ROC: {}'.format(pipe_dict[best_classifier]))



     






















    
    






ADA Boost Cross-Validation AUC-ROC: 0.87
XG Boost Cross-Validation AUC-ROC: 0.93
[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500










    
    






c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning: Found `num_boost_round` in params. Will use it instead of argument
  _log_warning(f"Found `{alias}` in params. Will use it instead of argument")
c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning: Found `num_boost_round` in params. Will use it instead of argument
  _log_warning(f"Found `{alias}` in params. Will use it instead of argument")










    
    






[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500










    
    






c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning: Found `num_boost_round` in params. Will use it instead of argument
  _log_warning(f"Found `{alias}` in params. Will use it instead of argument")










    
    






[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500










    
    






c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning: Found `num_boost_round` in params. Will use it instead of argument
  _log_warning(f"Found `{alias}` in params. Will use it instead of argument")










    
    






[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500










    
    






c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning: Found `num_boost_round` in params. Will use it instead of argument
  _log_warning(f"Found `{alias}` in params. Will use it instead of argument")










    
    






[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500
Light GBM Cross-Validation AUC-ROC: 0.92
Gradient Boost Cross-Validation AUC-ROC: 0.91
Learning rate set to 0.021203










    
    






Default metric period is 5 because AUC is/are not implemented for GPU










    
    






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1402:	total: 1m 16s	remaining: 5.31s
1403:	total: 1m 16s	remaining: 5.26s
1404:	total: 1m 16s	remaining: 5.2s
1405:	total: 1m 16s	remaining: 5.15s
1406:	total: 1m 17s	remaining: 5.09s
1407:	total: 1m 17s	remaining: 5.04s
1408:	total: 1m 17s	remaining: 4.98s
1409:	total: 1m 17s	remaining: 4.93s
1410:	total: 1m 17s	remaining: 4.87s
1411:	total: 1m 17s	remaining: 4.82s
1412:	total: 1m 17s	remaining: 4.76s
1413:	total: 1m 17s	remaining: 4.71s
1414:	total: 1m 17s	remaining: 4.65s
1415:	total: 1m 17s	remaining: 4.6s
1416:	total: 1m 17s	remaining: 4.54s
1417:	total: 1m 17s	remaining: 4.49s
1418:	total: 1m 17s	remaining: 4.43s
1419:	total: 1m 17s	remaining: 4.38s
1420:	total: 1m 17s	remaining: 4.32s
1421:	total: 1m 17s	remaining: 4.26s
1422:	total: 1m 17s	remaining: 4.21s
1423:	total: 1m 17s	remaining: 4.15s
1424:	total: 1m 17s	remaining: 4.1s
1425:	total: 1m 17s	remaining: 4.04s
1426:	total: 1m 18s	remaining: 3.99s
1427:	total: 1m 18s	remaining: 3.94s
1428:	total: 1m 18s	remaining: 3.88s
1429:	total: 1m 18s	remaining: 3.83s
1430:	total: 1m 18s	remaining: 3.77s
1431:	total: 1m 18s	remaining: 3.71s
1432:	total: 1m 18s	remaining: 3.66s
1433:	total: 1m 18s	remaining: 3.6s
1434:	total: 1m 18s	remaining: 3.55s
1435:	total: 1m 18s	remaining: 3.5s
1436:	total: 1m 18s	remaining: 3.44s
1437:	total: 1m 18s	remaining: 3.38s
1438:	total: 1m 18s	remaining: 3.33s
1439:	total: 1m 18s	remaining: 3.27s
1440:	total: 1m 18s	remaining: 3.22s
1441:	total: 1m 18s	remaining: 3.17s
1442:	total: 1m 18s	remaining: 3.11s
1443:	total: 1m 18s	remaining: 3.06s
1444:	total: 1m 18s	remaining: 3s
1445:	total: 1m 18s	remaining: 2.95s
1446:	total: 1m 18s	remaining: 2.89s
1447:	total: 1m 18s	remaining: 2.84s
1448:	total: 1m 19s	remaining: 2.78s
1449:	total: 1m 19s	remaining: 2.73s
1450:	total: 1m 19s	remaining: 2.67s
1451:	total: 1m 19s	remaining: 2.62s
1452:	total: 1m 19s	remaining: 2.56s
1453:	total: 1m 19s	remaining: 2.51s
1454:	total: 1m 19s	remaining: 2.45s
1455:	total: 1m 19s	remaining: 2.4s
1456:	total: 1m 19s	remaining: 2.34s
1457:	total: 1m 19s	remaining: 2.29s
1458:	total: 1m 19s	remaining: 2.23s
1459:	total: 1m 19s	remaining: 2.18s
1460:	total: 1m 19s	remaining: 2.12s
1461:	total: 1m 19s	remaining: 2.07s
1462:	total: 1m 19s	remaining: 2.02s
1463:	total: 1m 19s	remaining: 1.96s
1464:	total: 1m 19s	remaining: 1.91s
1465:	total: 1m 19s	remaining: 1.85s
1466:	total: 1m 19s	remaining: 1.8s
1467:	total: 1m 19s	remaining: 1.74s
1468:	total: 1m 19s	remaining: 1.69s
1469:	total: 1m 20s	remaining: 1.63s
1470:	total: 1m 20s	remaining: 1.58s
1471:	total: 1m 20s	remaining: 1.52s
1472:	total: 1m 20s	remaining: 1.47s
1473:	total: 1m 20s	remaining: 1.42s
1474:	total: 1m 20s	remaining: 1.36s
1475:	total: 1m 20s	remaining: 1.31s
1476:	total: 1m 20s	remaining: 1.25s
1477:	total: 1m 20s	remaining: 1.2s
1478:	total: 1m 20s	remaining: 1.14s
1479:	total: 1m 20s	remaining: 1.09s
1480:	total: 1m 20s	remaining: 1.03s
1481:	total: 1m 20s	remaining: 979ms
1482:	total: 1m 20s	remaining: 925ms
1483:	total: 1m 20s	remaining: 870ms
1484:	total: 1m 20s	remaining: 816ms
1485:	total: 1m 20s	remaining: 761ms
1486:	total: 1m 20s	remaining: 707ms
1487:	total: 1m 20s	remaining: 652ms
1488:	total: 1m 20s	remaining: 598ms
1489:	total: 1m 20s	remaining: 544ms
1490:	total: 1m 21s	remaining: 489ms
1491:	total: 1m 21s	remaining: 435ms
1492:	total: 1m 21s	remaining: 380ms
1493:	total: 1m 21s	remaining: 326ms
1494:	total: 1m 21s	remaining: 272ms
1495:	total: 1m 21s	remaining: 217ms
1496:	total: 1m 21s	remaining: 163ms
1497:	total: 1m 21s	remaining: 109ms
1498:	total: 1m 21s	remaining: 54.4ms
1499:	total: 1m 21s	remaining: 0us
Learning rate set to 0.021203
0:	total: 54.8ms	remaining: 1m 22s
1:	total: 110ms	remaining: 1m 22s
2:	total: 166ms	remaining: 1m 22s










    
    






Default metric period is 5 because AUC is/are not implemented for GPU










    
    






3:	total: 225ms	remaining: 1m 24s
4:	total: 289ms	remaining: 1m 26s
5:	total: 344ms	remaining: 1m 25s
6:	total: 412ms	remaining: 1m 27s
7:	total: 479ms	remaining: 1m 29s
8:	total: 533ms	remaining: 1m 28s
9:	total: 591ms	remaining: 1m 28s
10:	total: 643ms	remaining: 1m 27s
11:	total: 701ms	remaining: 1m 26s
12:	total: 763ms	remaining: 1m 27s
13:	total: 817ms	remaining: 1m 26s
14:	total: 873ms	remaining: 1m 26s
15:	total: 934ms	remaining: 1m 26s
16:	total: 994ms	remaining: 1m 26s
17:	total: 1.05s	remaining: 1m 26s
18:	total: 1.1s	remaining: 1m 26s
19:	total: 1.16s	remaining: 1m 25s
20:	total: 1.23s	remaining: 1m 26s
21:	total: 1.29s	remaining: 1m 26s
22:	total: 1.35s	remaining: 1m 26s
23:	total: 1.41s	remaining: 1m 26s
24:	total: 1.48s	remaining: 1m 27s
25:	total: 1.54s	remaining: 1m 27s
26:	total: 1.61s	remaining: 1m 27s
27:	total: 1.69s	remaining: 1m 28s
28:	total: 1.85s	remaining: 1m 34s
29:	total: 1.91s	remaining: 1m 33s
30:	total: 1.98s	remaining: 1m 33s
31:	total: 2.04s	remaining: 1m 33s
32:	total: 2.09s	remaining: 1m 33s
33:	total: 2.15s	remaining: 1m 32s
34:	total: 2.21s	remaining: 1m 32s
35:	total: 2.27s	remaining: 1m 32s
36:	total: 2.32s	remaining: 1m 31s
37:	total: 2.38s	remaining: 1m 31s
38:	total: 2.44s	remaining: 1m 31s
39:	total: 2.49s	remaining: 1m 30s
40:	total: 2.55s	remaining: 1m 30s
41:	total: 2.6s	remaining: 1m 30s
42:	total: 2.66s	remaining: 1m 30s
43:	total: 2.71s	remaining: 1m 29s
44:	total: 2.76s	remaining: 1m 29s
45:	total: 2.81s	remaining: 1m 28s
46:	total: 2.87s	remaining: 1m 28s
47:	total: 2.92s	remaining: 1m 28s
48:	total: 2.98s	remaining: 1m 28s
49:	total: 3.03s	remaining: 1m 27s
50:	total: 3.09s	remaining: 1m 27s
51:	total: 3.14s	remaining: 1m 27s
52:	total: 3.2s	remaining: 1m 27s
53:	total: 3.25s	remaining: 1m 27s
54:	total: 3.31s	remaining: 1m 26s
55:	total: 3.37s	remaining: 1m 26s
56:	total: 3.42s	remaining: 1m 26s
57:	total: 3.47s	remaining: 1m 26s
58:	total: 3.52s	remaining: 1m 25s
59:	total: 3.57s	remaining: 1m 25s
60:	total: 3.62s	remaining: 1m 25s
61:	total: 3.68s	remaining: 1m 25s
62:	total: 3.73s	remaining: 1m 25s
63:	total: 3.78s	remaining: 1m 24s
64:	total: 3.83s	remaining: 1m 24s
65:	total: 3.89s	remaining: 1m 24s
66:	total: 3.93s	remaining: 1m 24s
67:	total: 3.99s	remaining: 1m 23s
68:	total: 4.04s	remaining: 1m 23s
69:	total: 4.1s	remaining: 1m 23s
70:	total: 4.17s	remaining: 1m 23s
71:	total: 4.22s	remaining: 1m 23s
72:	total: 4.28s	remaining: 1m 23s
73:	total: 4.33s	remaining: 1m 23s
74:	total: 4.39s	remaining: 1m 23s
75:	total: 4.44s	remaining: 1m 23s
76:	total: 4.5s	remaining: 1m 23s
77:	total: 4.54s	remaining: 1m 22s
78:	total: 4.59s	remaining: 1m 22s
79:	total: 4.64s	remaining: 1m 22s
80:	total: 4.7s	remaining: 1m 22s
81:	total: 4.75s	remaining: 1m 22s
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83:	total: 4.85s	remaining: 1m 21s
84:	total: 4.9s	remaining: 1m 21s
85:	total: 4.95s	remaining: 1m 21s
86:	total: 5.01s	remaining: 1m 21s
87:	total: 5.05s	remaining: 1m 21s
88:	total: 5.1s	remaining: 1m 20s
89:	total: 5.15s	remaining: 1m 20s
90:	total: 5.2s	remaining: 1m 20s
91:	total: 5.25s	remaining: 1m 20s
92:	total: 5.3s	remaining: 1m 20s
93:	total: 5.35s	remaining: 1m 19s
94:	total: 5.39s	remaining: 1m 19s
95:	total: 5.45s	remaining: 1m 19s
96:	total: 5.5s	remaining: 1m 19s
97:	total: 5.54s	remaining: 1m 19s
98:	total: 5.59s	remaining: 1m 19s
99:	total: 5.64s	remaining: 1m 19s
100:	total: 5.7s	remaining: 1m 18s
101:	total: 5.75s	remaining: 1m 18s
102:	total: 5.8s	remaining: 1m 18s
103:	total: 5.84s	remaining: 1m 18s
104:	total: 5.89s	remaining: 1m 18s
105:	total: 5.95s	remaining: 1m 18s
106:	total: 6s	remaining: 1m 18s
107:	total: 6.04s	remaining: 1m 17s
108:	total: 6.09s	remaining: 1m 17s
109:	total: 6.14s	remaining: 1m 17s
110:	total: 6.19s	remaining: 1m 17s
111:	total: 6.25s	remaining: 1m 17s
112:	total: 6.3s	remaining: 1m 17s
113:	total: 6.35s	remaining: 1m 17s
114:	total: 6.4s	remaining: 1m 17s
115:	total: 6.45s	remaining: 1m 17s
116:	total: 6.5s	remaining: 1m 16s
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125:	total: 6.97s	remaining: 1m 15s
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136:	total: 7.53s	remaining: 1m 14s
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165:	total: 9s	remaining: 1m 12s
166:	total: 9.05s	remaining: 1m 12s
167:	total: 9.1s	remaining: 1m 12s
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170:	total: 9.25s	remaining: 1m 11s
171:	total: 9.3s	remaining: 1m 11s
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180:	total: 9.74s	remaining: 1m 10s
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297:	total: 16.4s	remaining: 1m 5s
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1416:	total: 1m 28s	remaining: 5.2s
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1432:	total: 1m 29s	remaining: 4.21s
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1452:	total: 1m 30s	remaining: 2.94s
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1468:	total: 1m 31s	remaining: 1.94s
1469:	total: 1m 31s	remaining: 1.87s
1470:	total: 1m 31s	remaining: 1.81s
1471:	total: 1m 31s	remaining: 1.75s
1472:	total: 1m 31s	remaining: 1.69s
1473:	total: 1m 32s	remaining: 1.62s
1474:	total: 1m 32s	remaining: 1.56s
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1477:	total: 1m 32s	remaining: 1.37s
1478:	total: 1m 32s	remaining: 1.31s
1479:	total: 1m 32s	remaining: 1.25s
1480:	total: 1m 32s	remaining: 1.18s
1481:	total: 1m 32s	remaining: 1.12s
1482:	total: 1m 32s	remaining: 1.06s
1483:	total: 1m 32s	remaining: 997ms
1484:	total: 1m 32s	remaining: 935ms
1485:	total: 1m 32s	remaining: 872ms
1486:	total: 1m 32s	remaining: 810ms
1487:	total: 1m 32s	remaining: 747ms
1488:	total: 1m 32s	remaining: 685ms
1489:	total: 1m 32s	remaining: 623ms
1490:	total: 1m 32s	remaining: 560ms
1491:	total: 1m 32s	remaining: 498ms
1492:	total: 1m 32s	remaining: 436ms
1493:	total: 1m 32s	remaining: 373ms
1494:	total: 1m 33s	remaining: 311ms
1495:	total: 1m 33s	remaining: 249ms
1496:	total: 1m 33s	remaining: 187ms
1497:	total: 1m 33s	remaining: 124ms
1498:	total: 1m 33s	remaining: 62.2ms
1499:	total: 1m 33s	remaining: 0us
Learning rate set to 0.021203
0:	total: 63.8ms	remaining: 1m 35s
1:	total: 138ms	remaining: 1m 43s










    
    






Default metric period is 5 because AUC is/are not implemented for GPU










    
    






2:	total: 202ms	remaining: 1m 40s
3:	total: 261ms	remaining: 1m 37s
4:	total: 317ms	remaining: 1m 34s
5:	total: 373ms	remaining: 1m 32s
6:	total: 432ms	remaining: 1m 32s
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1384:	total: 1m 11s	remaining: 5.93s
1385:	total: 1m 11s	remaining: 5.87s
1386:	total: 1m 11s	remaining: 5.82s
1387:	total: 1m 11s	remaining: 5.77s
1388:	total: 1m 11s	remaining: 5.72s
1389:	total: 1m 11s	remaining: 5.67s
1390:	total: 1m 11s	remaining: 5.62s
1391:	total: 1m 11s	remaining: 5.57s
1392:	total: 1m 11s	remaining: 5.51s
1393:	total: 1m 11s	remaining: 5.46s
1394:	total: 1m 11s	remaining: 5.41s
1395:	total: 1m 11s	remaining: 5.36s
1396:	total: 1m 11s	remaining: 5.31s
1397:	total: 1m 12s	remaining: 5.25s
1398:	total: 1m 12s	remaining: 5.2s
1399:	total: 1m 12s	remaining: 5.15s
1400:	total: 1m 12s	remaining: 5.1s
1401:	total: 1m 12s	remaining: 5.05s
1402:	total: 1m 12s	remaining: 4.99s
1403:	total: 1m 12s	remaining: 4.94s
1404:	total: 1m 12s	remaining: 4.89s
1405:	total: 1m 12s	remaining: 4.84s
1406:	total: 1m 12s	remaining: 4.79s
1407:	total: 1m 12s	remaining: 4.74s
1408:	total: 1m 12s	remaining: 4.69s
1409:	total: 1m 12s	remaining: 4.64s
1410:	total: 1m 12s	remaining: 4.58s
1411:	total: 1m 12s	remaining: 4.53s
1412:	total: 1m 12s	remaining: 4.48s
1413:	total: 1m 12s	remaining: 4.43s
1414:	total: 1m 12s	remaining: 4.38s
1415:	total: 1m 12s	remaining: 4.33s
1416:	total: 1m 12s	remaining: 4.28s
1417:	total: 1m 13s	remaining: 4.22s
1418:	total: 1m 13s	remaining: 4.17s
1419:	total: 1m 13s	remaining: 4.12s
1420:	total: 1m 13s	remaining: 4.07s
1421:	total: 1m 13s	remaining: 4.02s
1422:	total: 1m 13s	remaining: 3.96s
1423:	total: 1m 13s	remaining: 3.91s
1424:	total: 1m 13s	remaining: 3.86s
1425:	total: 1m 13s	remaining: 3.81s
1426:	total: 1m 13s	remaining: 3.76s
1427:	total: 1m 13s	remaining: 3.71s
1428:	total: 1m 13s	remaining: 3.65s
1429:	total: 1m 13s	remaining: 3.6s
1430:	total: 1m 13s	remaining: 3.55s
1431:	total: 1m 13s	remaining: 3.5s
1432:	total: 1m 13s	remaining: 3.44s
1433:	total: 1m 13s	remaining: 3.39s
1434:	total: 1m 13s	remaining: 3.34s
1435:	total: 1m 13s	remaining: 3.29s
1436:	total: 1m 13s	remaining: 3.24s
1437:	total: 1m 13s	remaining: 3.19s
1438:	total: 1m 13s	remaining: 3.13s
1439:	total: 1m 13s	remaining: 3.08s
1440:	total: 1m 14s	remaining: 3.03s
1441:	total: 1m 14s	remaining: 2.98s
1442:	total: 1m 14s	remaining: 2.93s
1443:	total: 1m 14s	remaining: 2.88s
1444:	total: 1m 14s	remaining: 2.83s
1445:	total: 1m 14s	remaining: 2.77s
1446:	total: 1m 14s	remaining: 2.72s
1447:	total: 1m 14s	remaining: 2.67s
1448:	total: 1m 14s	remaining: 2.62s
1449:	total: 1m 14s	remaining: 2.57s
1450:	total: 1m 14s	remaining: 2.52s
1451:	total: 1m 14s	remaining: 2.46s
1452:	total: 1m 14s	remaining: 2.41s
1453:	total: 1m 14s	remaining: 2.36s
1454:	total: 1m 14s	remaining: 2.31s
1455:	total: 1m 14s	remaining: 2.26s
1456:	total: 1m 14s	remaining: 2.21s
1457:	total: 1m 14s	remaining: 2.16s
1458:	total: 1m 14s	remaining: 2.1s
1459:	total: 1m 14s	remaining: 2.05s
1460:	total: 1m 14s	remaining: 2s
1461:	total: 1m 15s	remaining: 1.95s
1462:	total: 1m 15s	remaining: 1.9s
1463:	total: 1m 15s	remaining: 1.85s
1464:	total: 1m 15s	remaining: 1.79s
1465:	total: 1m 15s	remaining: 1.74s
1466:	total: 1m 15s	remaining: 1.69s
1467:	total: 1m 15s	remaining: 1.64s
1468:	total: 1m 15s	remaining: 1.59s
1469:	total: 1m 15s	remaining: 1.54s
1470:	total: 1m 15s	remaining: 1.49s
1471:	total: 1m 15s	remaining: 1.44s
1472:	total: 1m 15s	remaining: 1.38s
1473:	total: 1m 15s	remaining: 1.33s
1474:	total: 1m 15s	remaining: 1.28s
1475:	total: 1m 15s	remaining: 1.23s
1476:	total: 1m 15s	remaining: 1.18s
1477:	total: 1m 15s	remaining: 1.13s
1478:	total: 1m 15s	remaining: 1.07s
1479:	total: 1m 15s	remaining: 1.02s
1480:	total: 1m 15s	remaining: 973ms
1481:	total: 1m 15s	remaining: 922ms
1482:	total: 1m 15s	remaining: 871ms
1483:	total: 1m 15s	remaining: 819ms
1484:	total: 1m 16s	remaining: 768ms
1485:	total: 1m 16s	remaining: 717ms
1486:	total: 1m 16s	remaining: 666ms
1487:	total: 1m 16s	remaining: 614ms
1488:	total: 1m 16s	remaining: 563ms
1489:	total: 1m 16s	remaining: 512ms
1490:	total: 1m 16s	remaining: 461ms
1491:	total: 1m 16s	remaining: 410ms
1492:	total: 1m 16s	remaining: 358ms
1493:	total: 1m 16s	remaining: 307ms
1494:	total: 1m 16s	remaining: 256ms
1495:	total: 1m 16s	remaining: 205ms
1496:	total: 1m 16s	remaining: 154ms
1497:	total: 1m 16s	remaining: 102ms
1498:	total: 1m 16s	remaining: 51.2ms
1499:	total: 1m 16s	remaining: 0us
Learning rate set to 0.021203
0:	total: 51.9ms	remaining: 1m 17s
1:	total: 107ms	remaining: 1m 19s
2:	total: 160ms	remaining: 1m 19s










    
    






Default metric period is 5 because AUC is/are not implemented for GPU










    
    






3:	total: 213ms	remaining: 1m 19s
4:	total: 266ms	remaining: 1m 19s
5:	total: 320ms	remaining: 1m 19s
6:	total: 380ms	remaining: 1m 20s
7:	total: 438ms	remaining: 1m 21s
8:	total: 492ms	remaining: 1m 21s
9:	total: 545ms	remaining: 1m 21s
10:	total: 598ms	remaining: 1m 20s
11:	total: 651ms	remaining: 1m 20s
12:	total: 706ms	remaining: 1m 20s
13:	total: 762ms	remaining: 1m 20s
14:	total: 817ms	remaining: 1m 20s
15:	total: 875ms	remaining: 1m 21s
16:	total: 930ms	remaining: 1m 21s
17:	total: 982ms	remaining: 1m 20s
18:	total: 1.03s	remaining: 1m 20s
19:	total: 1.08s	remaining: 1m 20s
20:	total: 1.14s	remaining: 1m 20s
21:	total: 1.19s	remaining: 1m 19s
22:	total: 1.25s	remaining: 1m 20s
23:	total: 1.3s	remaining: 1m 19s
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25:	total: 1.42s	remaining: 1m 20s
26:	total: 1.48s	remaining: 1m 20s
27:	total: 1.54s	remaining: 1m 21s
28:	total: 1.6s	remaining: 1m 21s
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33:	total: 1.89s	remaining: 1m 21s
34:	total: 1.97s	remaining: 1m 22s
35:	total: 2.07s	remaining: 1m 24s
36:	total: 2.12s	remaining: 1m 23s
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41:	total: 2.4s	remaining: 1m 23s
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46:	total: 2.68s	remaining: 1m 22s
47:	total: 2.74s	remaining: 1m 22s
48:	total: 2.8s	remaining: 1m 22s
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50:	total: 2.92s	remaining: 1m 22s
51:	total: 2.99s	remaining: 1m 23s
52:	total: 3.04s	remaining: 1m 23s
53:	total: 3.09s	remaining: 1m 22s
54:	total: 3.15s	remaining: 1m 22s
55:	total: 3.2s	remaining: 1m 22s
56:	total: 3.26s	remaining: 1m 22s
57:	total: 3.31s	remaining: 1m 22s
58:	total: 3.37s	remaining: 1m 22s
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60:	total: 3.48s	remaining: 1m 22s
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67:	total: 3.89s	remaining: 1m 21s
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70:	total: 4.08s	remaining: 1m 22s
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72:	total: 4.2s	remaining: 1m 22s
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74:	total: 4.32s	remaining: 1m 22s
75:	total: 4.37s	remaining: 1m 21s
76:	total: 4.43s	remaining: 1m 21s
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194:	total: 10.9s	remaining: 1m 12s
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1177:	total: 1m 1s	remaining: 16.9s
1178:	total: 1m 1s	remaining: 16.9s
1179:	total: 1m 2s	remaining: 16.8s
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1181:	total: 1m 2s	remaining: 16.7s
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1187:	total: 1m 2s	remaining: 16.4s
1188:	total: 1m 2s	remaining: 16.3s
1189:	total: 1m 2s	remaining: 16.3s
1190:	total: 1m 2s	remaining: 16.2s
1191:	total: 1m 2s	remaining: 16.2s
1192:	total: 1m 2s	remaining: 16.1s
1193:	total: 1m 2s	remaining: 16.1s
1194:	total: 1m 2s	remaining: 16s
1195:	total: 1m 2s	remaining: 16s
1196:	total: 1m 2s	remaining: 15.9s
1197:	total: 1m 2s	remaining: 15.8s
1198:	total: 1m 2s	remaining: 15.8s
1199:	total: 1m 2s	remaining: 15.7s
1200:	total: 1m 2s	remaining: 15.7s
1201:	total: 1m 3s	remaining: 15.6s
1202:	total: 1m 3s	remaining: 15.6s
1203:	total: 1m 3s	remaining: 15.5s
1204:	total: 1m 3s	remaining: 15.5s
1205:	total: 1m 3s	remaining: 15.4s
1206:	total: 1m 3s	remaining: 15.4s
1207:	total: 1m 3s	remaining: 15.3s
1208:	total: 1m 3s	remaining: 15.2s
1209:	total: 1m 3s	remaining: 15.2s
1210:	total: 1m 3s	remaining: 15.1s
1211:	total: 1m 3s	remaining: 15.1s
1212:	total: 1m 3s	remaining: 15s
1213:	total: 1m 3s	remaining: 15s
1214:	total: 1m 3s	remaining: 14.9s
1215:	total: 1m 3s	remaining: 14.9s
1216:	total: 1m 3s	remaining: 14.8s
1217:	total: 1m 3s	remaining: 14.8s
1218:	total: 1m 3s	remaining: 14.7s
1219:	total: 1m 3s	remaining: 14.7s
1220:	total: 1m 3s	remaining: 14.6s
1221:	total: 1m 4s	remaining: 14.6s
1222:	total: 1m 4s	remaining: 14.5s
1223:	total: 1m 4s	remaining: 14.5s
1224:	total: 1m 4s	remaining: 14.4s
1225:	total: 1m 4s	remaining: 14.4s
1226:	total: 1m 4s	remaining: 14.3s
1227:	total: 1m 4s	remaining: 14.2s
1228:	total: 1m 4s	remaining: 14.2s
1229:	total: 1m 4s	remaining: 14.1s
1230:	total: 1m 4s	remaining: 14.1s
1231:	total: 1m 4s	remaining: 14s
1232:	total: 1m 4s	remaining: 14s
1233:	total: 1m 4s	remaining: 13.9s
1234:	total: 1m 4s	remaining: 13.9s
1235:	total: 1m 4s	remaining: 13.8s
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1237:	total: 1m 4s	remaining: 13.7s
1238:	total: 1m 4s	remaining: 13.7s
1239:	total: 1m 4s	remaining: 13.6s
1240:	total: 1m 4s	remaining: 13.6s
1241:	total: 1m 4s	remaining: 13.5s
1242:	total: 1m 5s	remaining: 13.4s
1243:	total: 1m 5s	remaining: 13.4s
1244:	total: 1m 5s	remaining: 13.3s
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1252:	total: 1m 5s	remaining: 12.9s
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1256:	total: 1m 5s	remaining: 12.7s
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1258:	total: 1m 5s	remaining: 12.6s
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1260:	total: 1m 6s	remaining: 12.5s
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1262:	total: 1m 6s	remaining: 12.4s
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1264:	total: 1m 6s	remaining: 12.3s
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1268:	total: 1m 6s	remaining: 12.2s
1269:	total: 1m 6s	remaining: 12.1s
1270:	total: 1m 6s	remaining: 12.1s
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1273:	total: 1m 7s	remaining: 11.9s
1274:	total: 1m 7s	remaining: 11.9s
1275:	total: 1m 7s	remaining: 11.8s
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1278:	total: 1m 7s	remaining: 11.6s
1279:	total: 1m 7s	remaining: 11.6s
1280:	total: 1m 7s	remaining: 11.5s
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1282:	total: 1m 7s	remaining: 11.4s
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1284:	total: 1m 7s	remaining: 11.3s
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1286:	total: 1m 7s	remaining: 11.2s
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1290:	total: 1m 8s	remaining: 11s
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1292:	total: 1m 8s	remaining: 10.9s
1293:	total: 1m 8s	remaining: 10.8s
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1295:	total: 1m 8s	remaining: 10.7s
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1297:	total: 1m 8s	remaining: 10.6s
1298:	total: 1m 8s	remaining: 10.6s
1299:	total: 1m 8s	remaining: 10.5s
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1301:	total: 1m 8s	remaining: 10.4s
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1305:	total: 1m 8s	remaining: 10.2s
1306:	total: 1m 8s	remaining: 10.2s
1307:	total: 1m 8s	remaining: 10.1s
1308:	total: 1m 8s	remaining: 10s
1309:	total: 1m 8s	remaining: 9.99s
1310:	total: 1m 8s	remaining: 9.94s
1311:	total: 1m 8s	remaining: 9.88s
1312:	total: 1m 9s	remaining: 9.83s
1313:	total: 1m 9s	remaining: 9.78s
1314:	total: 1m 9s	remaining: 9.73s
1315:	total: 1m 9s	remaining: 9.67s
1316:	total: 1m 9s	remaining: 9.62s
1317:	total: 1m 9s	remaining: 9.57s
1318:	total: 1m 9s	remaining: 9.51s
1319:	total: 1m 9s	remaining: 9.46s
1320:	total: 1m 9s	remaining: 9.41s
1321:	total: 1m 9s	remaining: 9.35s
1322:	total: 1m 9s	remaining: 9.3s
1323:	total: 1m 9s	remaining: 9.24s
1324:	total: 1m 9s	remaining: 9.19s
1325:	total: 1m 9s	remaining: 9.14s
1326:	total: 1m 9s	remaining: 9.08s
1327:	total: 1m 9s	remaining: 9.03s
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1329:	total: 1m 9s	remaining: 8.93s
1330:	total: 1m 9s	remaining: 8.87s
1331:	total: 1m 9s	remaining: 8.82s
1332:	total: 1m 9s	remaining: 8.77s
1333:	total: 1m 10s	remaining: 8.71s
1334:	total: 1m 10s	remaining: 8.66s
1335:	total: 1m 10s	remaining: 8.61s
1336:	total: 1m 10s	remaining: 8.56s
1337:	total: 1m 10s	remaining: 8.5s
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1339:	total: 1m 10s	remaining: 8.4s
1340:	total: 1m 10s	remaining: 8.34s
1341:	total: 1m 10s	remaining: 8.29s
1342:	total: 1m 10s	remaining: 8.24s
1343:	total: 1m 10s	remaining: 8.18s
1344:	total: 1m 10s	remaining: 8.13s
1345:	total: 1m 10s	remaining: 8.08s
1346:	total: 1m 10s	remaining: 8.03s
1347:	total: 1m 10s	remaining: 7.97s
1348:	total: 1m 10s	remaining: 7.92s
1349:	total: 1m 10s	remaining: 7.88s
1350:	total: 1m 10s	remaining: 7.82s
1351:	total: 1m 10s	remaining: 7.77s
1352:	total: 1m 11s	remaining: 7.72s
1353:	total: 1m 11s	remaining: 7.66s
1354:	total: 1m 11s	remaining: 7.61s
1355:	total: 1m 11s	remaining: 7.56s
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1357:	total: 1m 11s	remaining: 7.46s
1358:	total: 1m 11s	remaining: 7.41s
1359:	total: 1m 11s	remaining: 7.36s
1360:	total: 1m 11s	remaining: 7.31s
1361:	total: 1m 11s	remaining: 7.26s
1362:	total: 1m 11s	remaining: 7.2s
1363:	total: 1m 11s	remaining: 7.15s
1364:	total: 1m 11s	remaining: 7.1s
1365:	total: 1m 11s	remaining: 7.05s
1366:	total: 1m 11s	remaining: 7s
1367:	total: 1m 11s	remaining: 6.94s
1368:	total: 1m 12s	remaining: 6.89s
1369:	total: 1m 12s	remaining: 6.84s
1370:	total: 1m 12s	remaining: 6.79s
1371:	total: 1m 12s	remaining: 6.74s
1372:	total: 1m 12s	remaining: 6.68s
1373:	total: 1m 12s	remaining: 6.63s
1374:	total: 1m 12s	remaining: 6.58s
1375:	total: 1m 12s	remaining: 6.53s
1376:	total: 1m 12s	remaining: 6.47s
1377:	total: 1m 12s	remaining: 6.42s
1378:	total: 1m 12s	remaining: 6.37s
1379:	total: 1m 12s	remaining: 6.32s
1380:	total: 1m 12s	remaining: 6.26s
1381:	total: 1m 12s	remaining: 6.21s
1382:	total: 1m 12s	remaining: 6.16s
1383:	total: 1m 12s	remaining: 6.11s
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1386:	total: 1m 12s	remaining: 5.95s
1387:	total: 1m 13s	remaining: 5.89s
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1389:	total: 1m 13s	remaining: 5.79s
1390:	total: 1m 13s	remaining: 5.73s
1391:	total: 1m 13s	remaining: 5.69s
1392:	total: 1m 13s	remaining: 5.63s
1393:	total: 1m 13s	remaining: 5.58s
1394:	total: 1m 13s	remaining: 5.53s
1395:	total: 1m 13s	remaining: 5.48s
1396:	total: 1m 13s	remaining: 5.43s
1397:	total: 1m 13s	remaining: 5.37s
1398:	total: 1m 13s	remaining: 5.32s
1399:	total: 1m 13s	remaining: 5.27s
1400:	total: 1m 13s	remaining: 5.22s
1401:	total: 1m 13s	remaining: 5.17s
1402:	total: 1m 14s	remaining: 5.12s
1403:	total: 1m 14s	remaining: 5.07s
1404:	total: 1m 14s	remaining: 5.02s
1405:	total: 1m 14s	remaining: 4.97s
1406:	total: 1m 14s	remaining: 4.92s
1407:	total: 1m 14s	remaining: 4.86s
1408:	total: 1m 14s	remaining: 4.81s
1409:	total: 1m 14s	remaining: 4.76s
1410:	total: 1m 14s	remaining: 4.71s
1411:	total: 1m 14s	remaining: 4.65s
1412:	total: 1m 14s	remaining: 4.6s
1413:	total: 1m 14s	remaining: 4.55s
1414:	total: 1m 14s	remaining: 4.5s
1415:	total: 1m 14s	remaining: 4.44s
1416:	total: 1m 14s	remaining: 4.39s
1417:	total: 1m 15s	remaining: 4.34s
1418:	total: 1m 15s	remaining: 4.29s
1419:	total: 1m 15s	remaining: 4.24s
1420:	total: 1m 15s	remaining: 4.19s
1421:	total: 1m 15s	remaining: 4.14s
1422:	total: 1m 15s	remaining: 4.08s
1423:	total: 1m 15s	remaining: 4.03s
1424:	total: 1m 15s	remaining: 3.98s
1425:	total: 1m 15s	remaining: 3.92s
1426:	total: 1m 15s	remaining: 3.87s
1427:	total: 1m 15s	remaining: 3.82s
1428:	total: 1m 15s	remaining: 3.77s
1429:	total: 1m 15s	remaining: 3.72s
1430:	total: 1m 16s	remaining: 3.67s
1431:	total: 1m 16s	remaining: 3.61s
1432:	total: 1m 16s	remaining: 3.56s
1433:	total: 1m 16s	remaining: 3.51s
1434:	total: 1m 16s	remaining: 3.45s
1435:	total: 1m 16s	remaining: 3.4s
1436:	total: 1m 16s	remaining: 3.35s
1437:	total: 1m 16s	remaining: 3.29s
1438:	total: 1m 16s	remaining: 3.24s
1439:	total: 1m 16s	remaining: 3.19s
1440:	total: 1m 16s	remaining: 3.13s
1441:	total: 1m 16s	remaining: 3.08s
1442:	total: 1m 16s	remaining: 3.03s
1443:	total: 1m 16s	remaining: 2.97s
1444:	total: 1m 16s	remaining: 2.92s
1445:	total: 1m 16s	remaining: 2.87s
1446:	total: 1m 16s	remaining: 2.81s
1447:	total: 1m 16s	remaining: 2.76s
1448:	total: 1m 16s	remaining: 2.71s
1449:	total: 1m 17s	remaining: 2.65s
1450:	total: 1m 17s	remaining: 2.6s
1451:	total: 1m 17s	remaining: 2.55s
1452:	total: 1m 17s	remaining: 2.5s
1453:	total: 1m 17s	remaining: 2.44s
1454:	total: 1m 17s	remaining: 2.39s
1455:	total: 1m 17s	remaining: 2.33s
1456:	total: 1m 17s	remaining: 2.28s
1457:	total: 1m 17s	remaining: 2.23s
1458:	total: 1m 17s	remaining: 2.17s
1459:	total: 1m 17s	remaining: 2.12s
1460:	total: 1m 17s	remaining: 2.07s
1461:	total: 1m 17s	remaining: 2.02s
1462:	total: 1m 17s	remaining: 1.96s
1463:	total: 1m 17s	remaining: 1.91s
1464:	total: 1m 17s	remaining: 1.86s
1465:	total: 1m 17s	remaining: 1.8s
1466:	total: 1m 17s	remaining: 1.75s
1467:	total: 1m 17s	remaining: 1.7s
1468:	total: 1m 18s	remaining: 1.65s
1469:	total: 1m 18s	remaining: 1.59s
1470:	total: 1m 18s	remaining: 1.54s
1471:	total: 1m 18s	remaining: 1.49s
1472:	total: 1m 18s	remaining: 1.43s
1473:	total: 1m 18s	remaining: 1.38s
1474:	total: 1m 18s	remaining: 1.33s
1475:	total: 1m 18s	remaining: 1.27s
1476:	total: 1m 18s	remaining: 1.22s
1477:	total: 1m 18s	remaining: 1.17s
1478:	total: 1m 18s	remaining: 1.11s
1479:	total: 1m 18s	remaining: 1.06s
1480:	total: 1m 18s	remaining: 1.01s
1481:	total: 1m 18s	remaining: 954ms
1482:	total: 1m 18s	remaining: 901ms
1483:	total: 1m 18s	remaining: 848ms
1484:	total: 1m 18s	remaining: 795ms
1485:	total: 1m 18s	remaining: 742ms
1486:	total: 1m 18s	remaining: 689ms
1487:	total: 1m 18s	remaining: 636ms
1488:	total: 1m 18s	remaining: 583ms
1489:	total: 1m 18s	remaining: 530ms
1490:	total: 1m 18s	remaining: 477ms
1491:	total: 1m 19s	remaining: 424ms
1492:	total: 1m 19s	remaining: 371ms
1493:	total: 1m 19s	remaining: 318ms
1494:	total: 1m 19s	remaining: 265ms
1495:	total: 1m 19s	remaining: 212ms
1496:	total: 1m 19s	remaining: 159ms
1497:	total: 1m 19s	remaining: 106ms
1498:	total: 1m 19s	remaining: 52.9ms
1499:	total: 1m 19s	remaining: 0us
Learning rate set to 0.021203
0:	total: 52.6ms	remaining: 1m 18s
1:	total: 106ms	remaining: 1m 19s
2:	total: 162ms	remaining: 1m 20s










    
    






Default metric period is 5 because AUC is/are not implemented for GPU










    
    






3:	total: 211ms	remaining: 1m 18s
4:	total: 264ms	remaining: 1m 19s
5:	total: 319ms	remaining: 1m 19s
6:	total: 370ms	remaining: 1m 18s
7:	total: 426ms	remaining: 1m 19s
8:	total: 483ms	remaining: 1m 19s
9:	total: 539ms	remaining: 1m 20s
10:	total: 605ms	remaining: 1m 21s
11:	total: 659ms	remaining: 1m 21s
12:	total: 718ms	remaining: 1m 22s
13:	total: 785ms	remaining: 1m 23s
14:	total: 847ms	remaining: 1m 23s
15:	total: 901ms	remaining: 1m 23s
16:	total: 955ms	remaining: 1m 23s
17:	total: 1.01s	remaining: 1m 23s
18:	total: 1.07s	remaining: 1m 23s
19:	total: 1.13s	remaining: 1m 23s
20:	total: 1.18s	remaining: 1m 23s
21:	total: 1.23s	remaining: 1m 22s
22:	total: 1.29s	remaining: 1m 22s
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27:	total: 1.57s	remaining: 1m 22s
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42:	total: 2.4s	remaining: 1m 21s
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44:	total: 2.5s	remaining: 1m 20s
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46:	total: 2.66s	remaining: 1m 22s
47:	total: 2.74s	remaining: 1m 22s
48:	total: 2.84s	remaining: 1m 24s
49:	total: 2.91s	remaining: 1m 24s
50:	total: 2.96s	remaining: 1m 24s
51:	total: 3.03s	remaining: 1m 24s
52:	total: 3.12s	remaining: 1m 25s
53:	total: 3.21s	remaining: 1m 25s
54:	total: 3.29s	remaining: 1m 26s
55:	total: 3.35s	remaining: 1m 26s
56:	total: 3.41s	remaining: 1m 26s
57:	total: 3.46s	remaining: 1m 25s
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63:	total: 3.78s	remaining: 1m 24s
64:	total: 3.83s	remaining: 1m 24s
65:	total: 3.89s	remaining: 1m 24s
66:	total: 3.95s	remaining: 1m 24s
67:	total: 4s	remaining: 1m 24s
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70:	total: 4.17s	remaining: 1m 23s
71:	total: 4.21s	remaining: 1m 23s
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73:	total: 4.32s	remaining: 1m 23s
74:	total: 4.38s	remaining: 1m 23s
75:	total: 4.44s	remaining: 1m 23s
76:	total: 4.49s	remaining: 1m 23s
77:	total: 4.55s	remaining: 1m 22s
78:	total: 4.61s	remaining: 1m 22s
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80:	total: 4.71s	remaining: 1m 22s
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1498:	total: 5m 10s	remaining: 207ms
1499:	total: 5m 10s	remaining: 0us
Cat Boost Cross-Validation AUC-ROC: 0.92
[Voting] ...................... (1 of 4) Processing ada, total=   4.1s
[Voting] ...................... (2 of 4) Processing xgb, total=   7.7s
[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500










    
    






c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning: Found `num_boost_round` in params. Will use it instead of argument
  _log_warning(f"Found `{alias}` in params. Will use it instead of argument")










    
    






[Voting] ...................... (3 of 4) Processing lgb, total=   1.4s
Learning rate set to 0.021203
0:	total: 66.1ms	remaining: 1m 39s
1:	total: 127ms	remaining: 1m 35s










    
    






Default metric period is 5 because AUC is/are not implemented for GPU










    
    






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[Voting] ...................... (4 of 4) Processing cat, total= 1.4min
[Voting] ...................... (1 of 4) Processing ada, total=   4.3s
[Voting] ...................... (2 of 4) Processing xgb, total=   8.0s
[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500










    
    






c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning: Found `num_boost_round` in params. Will use it instead of argument
  _log_warning(f"Found `{alias}` in params. Will use it instead of argument")










    
    






[Voting] ...................... (3 of 4) Processing lgb, total=   1.9s
Learning rate set to 0.021203
0:	total: 56.8ms	remaining: 1m 25s
1:	total: 120ms	remaining: 1m 29s
2:	total: 193ms	remaining: 1m 36s










    
    






Default metric period is 5 because AUC is/are not implemented for GPU










    
    






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[Voting] ...................... (4 of 4) Processing cat, total= 1.4min
[Voting] ...................... (1 of 4) Processing ada, total=   4.6s
[Voting] ...................... (2 of 4) Processing xgb, total=   7.4s
[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500










    
    






c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning: Found `num_boost_round` in params. Will use it instead of argument
  _log_warning(f"Found `{alias}` in params. Will use it instead of argument")










    
    






[Voting] ...................... (3 of 4) Processing lgb, total=   1.8s
Learning rate set to 0.021203
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Default metric period is 5 because AUC is/are not implemented for GPU










    
    






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[Voting] ...................... (4 of 4) Processing cat, total= 1.3min
[Voting] ...................... (1 of 4) Processing ada, total=   4.8s
[Voting] ...................... (2 of 4) Processing xgb, total=   7.5s
[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500










    
    






c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning: Found `num_boost_round` in params. Will use it instead of argument
  _log_warning(f"Found `{alias}` in params. Will use it instead of argument")










    
    






[Voting] ...................... (3 of 4) Processing lgb, total=   1.6s
Learning rate set to 0.021203
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[Voting] ...................... (4 of 4) Processing cat, total= 1.6min
[Voting] ...................... (1 of 4) Processing ada, total=   8.9s
[Voting] ...................... (2 of 4) Processing xgb, total=   7.0s
[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500










    
    






c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning: Found `num_boost_round` in params. Will use it instead of argument
  _log_warning(f"Found `{alias}` in params. Will use it instead of argument")










    
    






[Voting] ...................... (3 of 4) Processing lgb, total=   2.3s
Learning rate set to 0.021203
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Default metric period is 5 because AUC is/are not implemented for GPU










    
    






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[Voting] ...................... (4 of 4) Processing cat, total= 1.4min
Voting Classifier Cross-Validation AUC-ROC: 0.93

Classifier with the best AUC-ROC: XG Boost




























We used a pipeline to determine the best classifier to use. We used a standard scaler and SMOTE for preprocessing. The models with the best cross validation scores on the training set was the voting classifier and XG Boost. Therefore, we will implement the voting classifier and measure the model against the test data set.























ADA Pipeline Feature Importance¶


















In [ ]:



     


# Ada pipeline feature importance
pipe_ada.fit(X_train, y_train)

ada_classifier = pipe_ada.named_steps['ada_classifier']
ada_importances = ada_classifier.feature_importances_
ada_indices = np.argsort(ada_importances)[::-1]

top_10_features = []
for f in range(10):
    feature_index = ada_indices[f]
    feature_name = X_train.columns[feature_index]
    top_10_features.append((feature_name, ada_importances[feature_index]))

ada_top_10_df = pd.DataFrame(top_10_features, columns=['Feature', 'Importance'])



     



















In [ ]:



     


# ADA feature imprtance
fig1 = px.pie(ada_top_10_df, names='Feature', values='Importance', title='ADA Boost Feature Importance', template='ggplot2')
fig1



     






















    
    


























In [ ]:



     


# fig1.write_html('figures/fig1.html')



     























XGBoost Pipeline Feature Importance¶


















In [ ]:



     


# XG Boost pipeline feature importance
pipe_xg.fit(X_train, y_train)

xg_classifier = pipe_xg.named_steps['xg_classifier']
xg_importances = xg_classifier.feature_importances_
xg_indices = np.argsort(xg_importances)[::-1]

top_10_features = []
for f in range(10):
    feature_index = xg_indices[f]
    feature_name = X_train.columns[feature_index]
    top_10_features.append((feature_name, xg_importances[feature_index]))

xg_top_10_df = pd.DataFrame(top_10_features, columns=['Feature', 'Importance'])



     



















In [ ]:



     


# XGB feature importance
fig2 = px.pie(xg_top_10_df, names='Feature', values='Importance', title='XG Boost Feature Importance', template='ggplot2')
fig2



     






















    
    


























In [ ]:



     


# fig2.write_html('figures/fig2.html')



     























LGBM Pipeline Feature Importance¶


















In [ ]:



     


# Light GBM pipeline feature importance
pipe_lgbm.fit(X_train, y_train)

lg_classifier = pipe_lgbm.named_steps['lgbm_classifier']
lg_importances = lg_classifier.feature_importances_
lg_indices = np.argsort(lg_importances)[::-1]

top_10_features = []
for f in range(10):
    feature_index = lg_indices[f]
    feature_name = X_train.columns[feature_index]
    top_10_features.append((feature_name, lg_importances[feature_index]))

lg_top_10_df = pd.DataFrame(top_10_features, columns=['Feature', 'Importance'])



     






















    
    






c:\Users\XIX\anaconda3\lib\site-packages\lightgbm\engine.py:177: UserWarning:

Found `num_boost_round` in params. Will use it instead of argument











    
    






[LightGBM] [Warning] num_iterations is set=500, num_boost_round=500 will be ignored. Current value: num_iterations=500
























In [ ]:



     


# LGBM feature importance
fig3 = px.pie(lg_top_10_df, names='Feature', values='Importance', title='Light GBM Feature Importance', template='ggplot2')
fig3



     






















    
    


























In [ ]:



     


# fig3.write_html('figures/fig3.html')



     























Gradient Boost Pipeline Feature Importance¶


















In [ ]:



     


# Gradient Boost pipeline feature importance
pipe_gb.fit(X_train, y_train)

gb_classifier = pipe_gb.named_steps['gb_classifier']
gb_importances = gb_classifier.feature_importances_
gb_indices = np.argsort(gb_importances)[::-1]

top_10_features = []
for f in range(10):
    feature_index = gb_indices[f]
    feature_name = X_train.columns[feature_index]
    top_10_features.append((feature_name, gb_importances[feature_index]))

gb_top_10_df = pd.DataFrame(top_10_features, columns=['Feature', 'Importance'])



     



















In [ ]:



     


# gb feature importance
fig4 = px.pie(gb_top_10_df, names='Feature', values='Importance', title='Gradient Boost Feature Importance', template='ggplot2')
fig4



     






















    
    


























In [ ]:



     


# fig4.write_html('figures/fig4.html')



     























CatBoost Pipeline Feature Importance¶


















In [ ]:



     


# Cat Boost pipeline feature importance
pipe_cat.fit(X_train, y_train)

cat_classifier = pipe_cat.named_steps['cat_classifier']
cat_importances = cat_classifier.feature_importances_
cat_indices = np.argsort(cat_importances)[::-1]

top_10_features = []
for f in range(10):
    feature_index = cat_indices[f]
    feature_name = X_train.columns[feature_index]
    top_10_features.append((feature_name, cat_importances[feature_index]))

cat_top_10_df = pd.DataFrame(top_10_features, columns=['Feature', 'Importance'])



     






















    
    






Learning rate set to 0.020944
0:	total: 58.7ms	remaining: 1m 28s
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Default metric period is 5 because AUC is/are not implemented for GPU










    
    






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In [ ]:



     


# cat boost feature importance
fig5 = px.pie(cat_top_10_df, names='Feature', values='Importance', title='Cat Boost Feature Importance', template='ggplot2')
fig5



     






















    
    


























In [ ]:



     


# fig5.write_html('figures/fig5.html')



     























Comparing Models¶


















In [ ]:



     


# series of model scores
data = {'Ada Boost': 0.87, 'XG Boost': 0.93 , 'LGBM': 0.92, 'Gradient Boost': 0.91, 'Cat Boost': 0.92, 'Voting Classifier': 0.93 }
comp = pd.Series(data, name='Auc-Roc Score')
comp



     






















    
    
Out[ ]:








Ada Boost            0.87
XG Boost             0.93
LGBM                 0.92
Gradient Boost       0.91
Cat Boost            0.92
Voting Classifier    0.93
Name: Auc-Roc Score, dtype: float64
























In [ ]:



     


# model scores
fig6 = px.scatter(comp, color=comp.index, size=comp, title='Model Comparison', symbol=comp, labels={'index': 'Model', 'value': 'AUC-ROC Score'})
fig6



     






















    
    


























In [ ]:



     


# fig6.write_html('figures/fig6.html')



     























Final Model¶


















In [ ]:



     


# Final model

class1 = AdaBoostClassifier(n_estimators=400, random_state=19, learning_rate=0.1)
class2 = GradientBoostingClassifier(n_estimators=1500, learning_rate=0.1, random_state=19, max_depth=5)
class3 = XGBClassifier(learning_rate=0.5, max_depth=5, n_estimators=1500) 
class4 = lgb.LGBMClassifier(objective='binary', metric='auc', boosting_type='gbdt', learning_rate=0.1, max_depth=20, n_estimators=350, num_leaves=30, random_state=19) 
class5 = CatBoostClassifier(task_type='GPU', loss_function='Logloss', eval_metric='AUC', iterations=1500, random_seed=19)
 

final = VotingClassifier(estimators=[('ada', class1), 
                                #('gbr', class2), 
                                ('xgb', class3), 
                                ('lgb', class4), 
                                ('cat', class5)], 
                                verbose=1, voting='soft')
final = final.fit(X_train, y_train)

# Make predictions on the test set
final_predictions = final.predict(X_test)

result = roc_auc_score(y_test, final_predictions)
print()
print("Voting Classifier model on the test set: ", result)



     






















    
    






[Voting] ...................... (1 of 4) Processing ada, total=   2.9s
[Voting] ...................... (2 of 4) Processing xgb, total=   6.1s
[Voting] ...................... (3 of 4) Processing lgb, total=   0.7s
Learning rate set to 0.021393










    
    






Default metric period is 5 because AUC is/are not implemented for GPU










    
    






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[Voting] ...................... (4 of 4) Processing cat, total= 1.2min

Voting Classifier model on the test set:  0.8531371081185577




























Final Model Evaluation Metric¶


















In [ ]:



     


# Display AUC ROC
probabilities_valid = final.predict_proba(X_test)
probabilities_one_valid = probabilities_valid[:, 1]

auc_roc = roc_auc_score(y_test, probabilities_one_valid)

print(auc_roc)

# ROC AUC curve of results
fpr, tpr, thresholds = roc_curve(y_test, probabilities_one_valid)

fig7 = px.area(
    x=fpr, y=tpr,
    title=f'ROC Curve (AUC={auc(fpr, tpr):.4f})',
    labels=dict(x='False Positive Rate', y='True Positive Rate'),
    width=800, height=600
)
fig7.add_shape(
    type='line', line=dict(dash='dash'),
    x0=0, x1=1, y0=0, y1=1
)

fig7.update_yaxes(scaleanchor="x", scaleratio=1)
fig7.update_xaxes(constrain='domain')
fig7.show()



     






















    
    






0.9408955174768223










    
    


























In [ ]:



     


# fig7.write_html('figures/fig7.html')



     























Final Model Feature Importances¶


















In [ ]:



     


# function for extracting feature importance of each model in voting classifier
def compute_feature_importance(voting_clf, weights):
    """ Function to compute feature importance of Voting Classifier """
    
    feature_importance = dict()
    for est in voting_clf.estimators_:
        feature_importance[str(est)] = est.feature_importances_
    
    fe_scores = [0]*len(list(feature_importance.values())[0])
    for idx, imp_score in enumerate(feature_importance.values()):
        imp_score_with_weight = imp_score*weights[idx]
        fe_scores = list(np.add(fe_scores, list(imp_score_with_weight)))
    return fe_scores



     



















In [ ]:



     


# creating feature importance dataframe
fedf = pd.DataFrame()
fedf['Feature'] = X_train.columns
fedf['Feature Importance'] = compute_feature_importance(final, [1, 1, 1, 1])
fedf = fedf.sort_values('Feature Importance', ascending=False).head(10)



     



















In [ ]:



     


# voting classifier feature importance
fig8 = px.pie(fedf, names='Feature', values='Feature Importance', title='Top 10 Features', template='ggplot2')
fig8



     






















    
    


























In [ ]:



     


# fig8.write_html('figures/fig8.html')



     























Overall Conclusions¶






















We succeeded in achieving our target AUC ROC score of 0.75. Our final score with the test set is 0.9409, which is roughly 25% more than our target. The boosting models generally had good scores with the training set, but to ensure a better score with the test set, we used a voting classifier. We also looked into the feature importance of the various models in our pipeline, and finally, the important features in our final model. We see that total charges and monthly charges are the most important features, followed by the time frame of the account opening with the year and month. It is reasonable to conclude features such as charges and the length of time a customer has had service, outweigh other factors such as demographics.


Therefore, Interconnect can utilize this model to predict churn. Alongside marketing strategies, the company can target customers likely to leave, and therefore implement strategies to prevent churn. They may also be interested in looking at trends in the charges of customers alongside the length of service, to periodically implement marketing strategies to prevent churn.























Solution Report¶






















All steps of the final work plan were performed, yet we could have done without the K Means analysis, as the results of that model were difficult to interpret.


Since we used a voting classifier as our final model, we found it difficult to illustrate the inner workings of this black box model. Therefore, we did extensive research to write a code that could extract the feature importance of each model in the ensemble, and give the average feature importance as the overall summary of the model.


Key steps to a successful model and prediction were parsing the start dates into months and years. Furthermore, scaling the dataset and using SMOTE in the pipeline likely helped to achieve a good AUC-ROC score. The final model is a voting classifier with an AUC-ROC score of 0.9409. Choosing the voting classifier was a good decision, yet and XG Boost classifier may have been equally as good.